code: def __init__(self, config):
      super().__init__(config)
      if config["lagrangian"]:
          # Eager mode.
          if config["framework"] == "tf2":
              self._alpha_prime_optimizer = tf.keras.optimizers.Adam(
                  learning_rate=config["optimization"][
                      "critic_learning_rate"
                  ]
              )
          # Static graph mode.
          else:
              self._alpha_prime_optimizer = tf1.train.AdamOptimizer(
                  learning_rate=config["optimization"][
                      "critic_learning_rate"
                  ]
              )
  
imports: from functools import partial
  import numpy
  import gymnasium
  import logging
  import tree
  from typing import Dict, List, Type, Union
  import ray
  import ray.experimental.tf_utils
  from ray.rllib.algorithms.sac.sac_tf_policy import apply_gradients, compute_and_clip_gradients, get_distribution_inputs_and_class, _get_dist_class, build_sac_model, postprocess_trajectory, setup_late_mixins, stats, validate_spaces, ActorCriticOptimizerMixin, ComputeTDErrorMixin
  from ray.rllib.models.modelv2 import ModelV2
  from ray.rllib.models.tf.tf_action_dist import TFActionDistribution
  from ray.rllib.policy.tf_mixins import TargetNetworkMixin
  from ray.rllib.policy.tf_policy_template import build_tf_policy
  from ray.rllib.policy.policy import Policy
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.utils.exploration.random import Random
  from ray.rllib.utils.framework import get_variable, try_import_tf, try_import_tfp
  from ray.rllib.utils.typing import LocalOptimizer, ModelGradients, TensorType, AlgorithmConfigDict
label_desc: Use image node_id 1 to create a new ActorCriticOptimizerMixin object from inherited base classes: SACActorCriticOptimizerMixin with example: obj = ActorCriticOptimizerMixin(config)